Systems and methods for searching for events within video content

ABSTRACT

A video management system (VMS) may search for one or more events in a plurality of video streams captured and stored at a plurality of remote sites. The VMS may generate time-stamped metadata for each video stream captured at the remote site. The time-stamped metadata for each video stream may identify one or more objects and/or events occurring in the corresponding video stream as well as an identifier that uniquely identifies the corresponding video stream. Each of the plurality of remote sites may send the time-stamped metadata to a central hub, wherein the time-stamped metadata may be stored in a data lake, and a user may enter a query into a video query engine, wherein the video query engine may be operatively coupled to the central hub.

This is a continuation of co-pending U.S. patent application Ser. No.16/792,818, filed Feb. 17, 2020, and entitled SYSTEMS AND METHODS FORSEARCHING FOR EVENTS WITHIN VIDEO CONTENT, which is incorporated hereinby reference.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is related to U.S. application Ser. No.16/792,852, filed on Feb. 17, 2020, and entitled SYSTEMS AND METHODS FOREFFICIENTLY SENDING VIDEO METADATA, now U.S. Pat. No. 11,030,240, andU.S. application Ser. No. 16/792,860, filed on Feb. 17, 2020, andentitled SYSTEMS AND METHODS FOR IDENTIFYING EVENTS WITHIN VIDEO CONTENTUSING INTELLIGENT SEARCH QUERY.

TECHNICAL FIELD

The present disclosure relates generally to video management systems,and more particularly, to video management systems that utilizeintelligent video queries.

BACKGROUND

Known video management systems (VMS) used in security surveillance andthe like can include a plurality of cameras. In some cases, videomanagement systems are used to monitor areas such as, for example,banks, stadiums, shopping centers, airports, and the like. In somecases, video management systems may store captured video content locallyand/or remotely, sometimes using one or more video management servers.Searching the video content for one or more events can be resourceintensive. What would be desirable is a more efficient way of capturing,organizing and/or processing video content to help identify one or moreevents in the captured video.

SUMMARY

The present disclosure relates generally to video management systems,and more particularly, to video management systems that provide a moreefficient way of capturing, organizing and/or processing video contentto help identify one or more events in the captured video.

In one example, a method for searching for one or more events in aplurality of video streams captured and stored at a plurality of remotesites may include generating, at each of the plurality of remote sites,time-stamped metadata for each video stream captured at the remote site.The time-stamped metadata for each video stream may identify one or moreobjects and/or events occurring in the corresponding video stream aswell as an identifier that uniquely identifies the corresponding videostream. Each of the plurality of remote sites may send the time-stampedmetadata to a central hub, wherein the time-stamped metadata may bestored in a data lake. A user may enter a query into a video queryengine, wherein the video query engine may be operatively coupled to thecentral hub. The query may be applied to the time-stamped metadatastored in the data lake to search for one or more objects and/or eventsin the plurality of video streams that match the query. A search resultmay be returned to the user, wherein the search result may identify oneor more matching objects and/or events in the plurality of video streamsthat match the query, and for each matching object and/or event thatmatches the query, a link to the corresponding video stream with areference time that includes the matching object and/or event may beprovided. The link may be used to download a video clip of the videostream that includes the matching object and/or event from thecorresponding remote site, and the video clip of the video stream may bedisplayed on a display.

In another example, a central hub for searching for one or more eventsin a plurality of video streams captured and stored at a plurality ofremote sites may include, a memory, and one or more processorsoperatively coupled to the memory, which may be configured to: receivetime-stamped metadata from the plurality of remote sites, wherein foreach remote site, the time-stamped metadata may be received for eachvideo stream captured at the remote site, and the time-stamped metadatafor each video stream may identify one or more objects and/or eventsoccurring in the corresponding video stream as well as an identifierthat uniquely identifies the corresponding video stream. The one or moreprocessors may store the received time-stamped metadata in the memory,receive a query from a user, apply the query to the time-stampedmetadata in the memory to search for one or more objects and/or eventsin the plurality of video streams that match the query, and return asearch result, wherein the search result may identify one or morematching objects and/or events in the plurality of video streams thatmatch the query, and for each matching object and/or event that matchesthe query, provide a link to the corresponding video stream stored at acorresponding remote site with a reference time that includes thematching object and/or event. The processor may use the link to downloada video clip of the video stream that includes the matching objectand/or event from the corresponding remote site, and output the videoclip of the video stream for display.

In another example, a remote site capturing one or more video streamsmay include a memory, and one or more processors operatively coupled tothe memory, which may be configured to: store the one or more videostreams captured at the remote site in the memory, generate time-stampedmetadata for each of the one or more video streams captured at theremote site, the time-stamped metadata for each video stream identifyingone or more objects and/or events occurring in the corresponding videostream as well as an identifier that uniquely identifies thecorresponding video stream, send the time-stamped metadata to a centralhub that is remote from the remote site, receive a request from thecentral hub, the request may identify a particular one of the one ormore video streams and a reference time, and send a video clip of therequested video stream that includes the reference time.

The preceding summary is provided to facilitate an understanding of someof the innovative features unique to the present disclosure and is notintended to be a full description. A full appreciation of the disclosurecan be gained by taking the entire specification, claims, figures, andabstract as a whole.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure may be more completely understood in consideration of thefollowing description of various examples in connection with theaccompanying drawings, in which:

FIG. 1 is a schematic view of an illustrative video management systemhaving an illustrative cloud tenant within a cloud in communication withone or more remotely located sites;

FIG. 2 is a schematic block diagram of the illustrative cloud tenant ofFIG. 1 ;

FIG. 3 is a schematic view of an illustrative video query engine of theillustrative cloud tenant of FIG. 2 in communication with a securityanalyst;

FIG. 4 is a schematic block diagram showing an illustrative SpatialTemporal Regional Graph (STRG) method illustrating receiving and storingmetadata from one or more remotely located sites;

FIG. 5 is a schematic block diagram showing an illustrative method forreturning a search result based on a video query of the stored metadata;

FIG. 6 is a flow diagram showing an illustrative method for sendingtime-stamped metadata corresponding to a video stream across acommunication path having a limited bandwidth;

FIG. 7A shows an illustrative scene including one or more objects;

FIG. 7B shows the illustrative scene of FIG. 7A, in which the one ormore objects have changed position;

FIG. 7C shows the illustrative scene of FIG. 7A, in which the one ormore objects have changed position;

FIG. 8 is a flow diagram showing an illustrative method for searchingfor one or more objects and/or events in one or more video streams;

FIG. 9 is a schematic block diagram showing an illustrative method forintelligent machine learning;

FIG. 10 is a schematic block diagram showing an illustrative method forintelligent machine learning;

FIG. 11A shows an illustrative screen in which a user may enter a queryto search for one or more objects and/or events in one or more videostreams;

FIG. 11B shows an illustrative output of the query entered in FIG. 11A;

FIG. 12 is a flow diagram showing an illustrative method for searchingfor one or more events in a plurality of video streams captured andstored at a plurality of remote sites;

FIG. 13 is a flow diagram showing an illustrative method for receivingtime-stamped metadata corresponding to a video stream across acommunication path having a limited bandwidth; and

FIG. 14 is a flow diagram showing an illustrative method for searchingfor one or more objects and/or events in one or more video streams.

While the disclosure is amenable to various modifications andalternative forms, specifics thereof have been shown by way of examplein the drawings and will be described in detail. It should beunderstood, however, that the intention is not to limit the disclosureto the particular examples described. On the contrary, the intention isto cover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the disclosure.

DESCRIPTION

The following description should be read with reference to the drawings,in which like elements in different drawings are numbered in likefashion. The drawings, which are not necessarily to scale, depictexamples that are not intended to limit the scope of the disclosure.Although examples are illustrated for the various elements, thoseskilled in the art will recognize that many of the examples providedhave suitable alternatives that may be utilized.

All numbers are herein assumed to be modified by the term “about”,unless the content clearly dictates otherwise. The recitation ofnumerical ranges by endpoints includes all numbers subsumed within thatrange (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, and 5).

As used in this specification and the appended claims, the singularforms “a”, “an”, and “the” include the plural referents unless thecontent clearly dictates otherwise. As used in this specification andthe appended claims, the term “or” is generally employed in its senseincluding “and/or” unless the content clearly dictates otherwise.

It is noted that references in the specification to “an embodiment”,“some embodiments”, “other embodiments”, etc., indicate that theembodiment described may include a particular feature, structure, orcharacteristic, but every embodiment may not necessarily include theparticular feature, structure, or characteristic. Moreover, such phrasesare not necessarily referring to the same embodiment. Further, when aparticular feature, structure, or characteristic is described inconnection with an embodiment, it is contemplated that the feature,structure, or characteristic is described in connection with anembodiment, it is contemplated that the feature, structure, orcharacteristic may be applied to other embodiments whether or notexplicitly described unless clearly stated to the contrary.

The present disclosure relates generally to video management systemsused in connection with surveillance systems. Video management systemscan include, for example, a network connected device, network equipment,a remote monitoring station, a surveillance system deployed in a securearea, a closed circuit television (CCTV), security cameras, networkedvideo recorders, and/or panel controllers. In some cases, videomanagement systems may be used to monitor large areas such as, forexample, banks, stadiums, shopping centers, parking lots, airports, andthe like, and may be capable of producing 10,000 or more video clips perday. These are just examples. While video surveillance systems are usedas an example, it is contemplated that the present disclosure may beused in conjunction with any suitable video based system.

FIG. 1 is a schematic view of an illustrative video management system(VMS) 10 having an illustrative cloud tenant 20 within the cloud 14, incommunication with one or more remotely located sites 12 a, 12 b, and 12c (hereinafter generally referenced as sites 12). The sites 12 may begeographically dispersed and/or may be located within one building orarea to be monitored. While a total of three sites 12 are shown, it willappreciated that this is merely illustrative, as there may be any numberof remotely located sites 12. As shown in FIG. 1 , a security analyst 16may monitor the sites 12 from a workstation 15 which may be remotelylocated from the sites 12. However, in some cases, the security analyst16 may be located at one of the sites 12.

The workstation 15 may be configured to communicate with the cloudtenant 20, which may include one or more video processing controllersand a memory (e.g., memory 60 as shown in FIG. 2 ). The cloud tenant 20may act as a central hub, and may be configured to control one or morecomponents of the video management system 10. The workstation 15 maycommunicate with the cloud tenant 20 via a wired or wireless link (notshown).

Additionally, the cloud tenant 20 may communicate over one or more wiredor wireless networks that may accommodate remote access and/or controlof the cloud tenant 20 via another device such as a smart phone, tablet,e-reader, laptop computer, personal computer, or the like. In somecases, the network may be a wireless local area network (LAN). In somecases, the network may be a wide area network or global network (WAN)including, for example, the Internet. In some cases, the wireless localarea network may provide a wireless access point and/or a network hostdevice that is separate from the video processing controller. In othercases, the wireless local area network may provide a wireless accesspoint and/or a network host device that is part of the cloud tenant 20.In some cases, the wireless local area network may include a localdomain name server (DNS), but this is not required for all embodiments.In some cases, the wireless local area network may be an ad-hoc wirelessnetwork, but this is not required.

In some cases, the cloud tenant 20 may be programmed to communicate overthe network with an external web service hosted by one or more externalweb server(s). The cloud tenant 20 may be configured to upload selecteddata via the network to the external web service where it may becollected and stored on the external web server. In some cases, the datamay be indicative of the performance of the video management system 10.Additionally, the cloud tenant 20 may be configured to receive and/ordownload selected data, settings and/or services sometimes includingsoftware updates from the external web service over the network. Thedata, settings and/or services may be received automatically from theweb service, downloaded periodically in accordance with a controlalgorithm, and/or downloaded in response to a user request.

Depending upon the application and/or where the video management systemuser is located, remote access and/or control of the cloud tenant 20 maybe provided over a first network and/or a second network. A variety ofremote wireless devices may be used to access and/or control the cloudtenant 20 from a remote location (e.g., remote from the cloud tenant 20)over the first network and/or the second network including, but notlimited to, mobile phones including smart phones, tablet computers,laptop or personal computers, wireless network-enabled key fobs,e-readers, and/or the like. In many cases, the remote wireless devicesare configured to communicate wirelessly over the first network and/orsecond network with the cloud tenant 20 via one or more wirelesscommunication protocols including, but not limited to, cellularcommunication, ZigBee, REDLINK™, Bluetooth, WiFi, IrDA, dedicated shortrange communication (DSRC), EnOcean, and/or any other suitable common orproprietary wireless protocol, as desired.

The cloud tenant 20 may be in communication with the sites 12 via awired and/or wireless link (not shown). The remotely located sites 12may each include a plurality of video surveillance cameras, which may belocated along a periphery or scattered throughout an area that is beingmonitored by the cameras. The cameras may include closed circuittelevision (CCTV) hardware, such as security cameras, networked videorecorders, panel controllers, and/or any other suitable camera. Thecameras may be controlled via a control panel that may, for example, bepart of the cloud tenant 20. In some instances, the control panel (notillustrated) may be distinct form the cloud tenant 20, and may insteadbe part of, for example, a server that is local to the particular site12. As shown, the cloud tenant 20 may be remote from the cameras and/orthe sites 12. The cloud tenant 20 may operate under the control of oneor more programs loaded from a non-transitory computer-readable, such asa memory.

The sites 12 may further include one or more workstations (e.g.,workstation 15), which may be used to display images provided by thecameras to security personnel (e.g., security analyst 16), for example,on a display (not shown). The workstation 15 may be a personal computer,for example, or may be a terminal connected to a cloud-based processingsystem (e.g., cloud tenant 20). In some cases, the cloud tenant 20 mayreceive one or more images from the sites 12 and may process the imagesto enable easer search and discovery of content contained within theimages. While discussed with respect to processing live or substantiallylive video feeds, it will be appreciated that stored images such asplaying back video feeds, or even video clips, may be similarlyprocessed. The cloud tenant 20 may also receive commands or otherinstructions from a remote location such as, for example, workstation15, via an input/output (I/O). The cloud tenant 20 may be configured tooutput processed images to portable devices via the cloud 14, and/or tothe workstation 15 via the I/O.

In some cases, the cloud tenant 20 may include metadata message brokerswhich may listen for messages from sites 12. The message brokers maysend the metadata to storage centers within the cloud tenant 20. A videoquery may be generated by the security analyst 16, and one or morecomponents (e.g., one or more processors) within the cloud tenant 20 mayapply the video query to the metadata and produce an output. Forexample, the sites 12 may receive video streams from the plurality ofvideo surveillance cameras, and may generate time-stamped metadata foreach video stream captured at each respective site (e.g., sites 12). Thetime-stamped metadata may then be stored in a memory at each respectivesite (e.g., sites 12). In some cases, the time-stamped metadata may besent to a central hub (e.g., the cloud tenant 20) and the metadata maybe stored within the cloud tenant 20. In some cases, the video contentis not sent to the cloud tenant, at least initially, but rather only themetadata is sent. This reduced the bandwidth required to support thesystem.

In some cases, the one or more components (e.g., one or more processors)of the control hub (e.g. the cloud tenant 20) may process metadatastored within the cloud tenant 20 to identify additional objects and/orevents occurring in the plurality of video streams captures at theplurality of sites 12. A user (e.g., the security analyst 16) may thenenter a query, and the query may be applied to the metadata storedwithin the cloud tenant 20. The cloud tenant 20 may then return a searchresult to the user which identifies one or more matching objects and/orevents within the stored video streams that match that entered query.

In some cases, the entered query may be entered into a video queryengine (e.g., video query engine 25). The video query engine may applythe query to the stored time-stamped metadata and search for events thatmatches the query. The video query engine may return a search result tothe user, and in some cases, the user may provide feedback to indicatewhether the search result accurately represent what the user intendedwhen entering the query. For example, the user feedback may include asubsequent user query that is entered after the video query enginereturns the search result. The video query engine may include one ormore cognitive models (e.g., video cognitive interfaces 28 and videocognitive services 29), which may be refined using machine learning overtime based on the user feedback.

As discussed, the sites 12 may receive video streams from the pluralityof video surveillance cameras, and may generate time-stamped metadatafor each video stream captured at each respective site (e.g., sites 12).The generated time-stamped metadata for each video stream may identifyone or more objects and/or events occurring in the corresponding videosteam as well as an identifier that uniquely identifies thecorresponding video stream. The sites 12 may include one or moreprocessors operatively coupled to memory. The time-stamped metadata maybe stored in the memory at each respective site (e.g., sites 12).Subsequently, the time-stamped metadata may be sent to a central hub,such as for example, the cloud tenant 20, which may be remote from thesites 12. In some cases, a site 12 may receive a request from thecentral hub (e.g., the cloud tenant 20) identifying a particular one ofthe one or more video streams and a reference time. In response, theremote site 12 may send a video clip that matches the request from thecentral hub. The request may include an identifier that uniquelyidentifies the corresponding video stream source and a requestedreference time.

FIG. 2 is a schematic block diagram of the illustrative cloud tenant 20.In some cases, as discussed with reference to FIG. 1 , the cloud tenant20 may receive metadata 18, which may include time-stamped metadata,from each of the plurality of remote sites 12. The cloud tenant 20 mayinclude a memory 60 and one or more processors 61. The memory 60 maytemporarily store the time-stamped metadata 18 received from each of theplurality of sites 12. The memory 60 may be any suitable type of storagedevice including, but not limited to, RAM, ROM, EPROM, flash memory, ahard drive, and/or the like. The one or more processors 61 may beoperatively coupled to the memory 60, and may be configured to receivetime-stamped metadata 18 from the plurality of remote sites 12. The oneor more processors 61 may include a plurality of processors or serviceswithin the cloud tenant 20 (e.g., IoT applications such as, for example,IoT hub, event hub, video query engine, service discovery agent, etc.).The time-stamped metadata 18 received from each of the plurality ofremote sites 12 may identify one or more objects and/or events occurringin the corresponding video stream, as well as an identifier thatuniquely identifies the corresponding video stream, as will be discussedfurther with reference to FIGS. 7A-7C and 12 .

As discussed, the metadata 18 may identify one or more objects and/orevents occurring in the corresponding video stream. The metadata 18 maybe sent from the sites 12 to an IoT hub 21 and/or to an event hub 22.The IoT hub 21 may collect a large volume of metadata 18 from theplurality of sites 12, and may act as a central message hub forbi-directional communication between applications and the devices itmanages, as well as communication both from the sites 12 to the cloudtenant 20 and the cloud tenant 20 to the sites 12. The IoT hub 21 maysupport multiple messaging modes such as site to cloud tenant 20,metadata 18 file upload from the sites, and request-reply methods tocontrol the overall video management system 10 from the cloud 14. TheIoT hub 21 may further maintain the health of the cloud tenant 20 bytracking events such as site creation, site failures, and siteconnections.

In some cases, the event hub 22 may be configured to receive and processmillions of video events per second (e.g., metadata 18). The metadata 18sent to the event hub 22 may be transformed and stored using real-timeanalytics and/or batching/storage adapters. The event hub 22 may providea distributed stream processing platform and may identify behaviorswithin the metadata 18. For example, the identified behaviors mayinclude, event-pattern detection, event abstraction, event filtering,event aggregation and transformation, modeling event hierarchies,detecting event relationships, and abstracting event-driven processes.The event hub 22 may be coupled with the IoT hub 21 such that themetadata 18 processed by the event hub 22 may be sent to a message bus24 within the cloud tenant 20. Further, the event hub 22 may be coupledto a fast time series store 23, which may be configured to storetime-stamped metadata 18 upon receiving the metadata 18 from the eventhub 22. The time-stamped metadata 18 may include measurements or eventsthat are tracked, monitored, down sampled, and/or aggregated over time.The fast time series store 23 may be configured to store the metadata 18using metrics or measurements that are time-stamped such that one ormore math models may be applied on top of the metadata 18. In somecases, the fast time series store 23 may measure changes over time.

As discussed, the IoT hub 21 may communicate with the message bus 24.The message bus 24 may include a combination of a data model, a commandset, and a messaging infrastructure to allow multiple systems tocommunicate through a shared set of interfaces. In the cloud tenant 20,the message bus 24 may be configured to operate as a processing engineand a common interface between the various IoT applications, which mayoperate together using publisher/subscriber (e.g., pub/sub 58)interfaces. This may belp the cloud tenant 20 scale horizontally as wellas vertically, which may belp the cloud tenant 20 meet demand as thenumber of sites 12 increases. The message bus 24 may further manage datain motion, and may aid with cyber security and fraud management of thecloud tenant 20. In some cases, the security analyst 16 may enter asearch query into the video management system 10. The query may bereceived by the message bus 24 within the cloud tenant 20, andsubsequently sent to a video query engine 25.

The video query engine 25 may be a core component of the cloud tenant20. The video query engine 25 may communicate with a video data lake 31,an analytics model store 32, and may be driven by a connected videomicro services 30. The video data lake may be part of the memory 60, ormay be separate. In some instances, the video query engine 25 mayinclude a publisher/subscriber, e.g., pub/sub 58, which may beconfigured to communicate with various applications that may subscribeto the metadata 18 within the cloud tenant 20. The pub/sub 58 may beconnected to the message bus 24 to maintain independence and loadbalancing capabilities. The video query engine 25 may further includeone or more video cognitive interfaces 28, one or more video cognitiveservices 29, and one or more machine learning applications 59. The videoquery engine 25 may be a high fidelity video query engine that may beconfigured to create a summary of the video data streams, which may begenerated by applying a search query to the metadata 18. The summary mayinclude one or more results which match the search query, and furthermay associate each one of the one or more results with the correspondingsite 12 from which the video data stream is from. The one or more videocognitive services 29 may be configured to provide various inferencesabout the search query entered. The inferences may include, for example,a user's intent for the query, a user's emotion, a type of user, a typeof situation, a resolution, and a situational or other context for thequery.

As stated, the video query engine 25 may be driven by the connectedvideo micro-services 30. The video micro-services 30 may be the coreservice orchestration layer within the cloud tenant 20, in which aplurality of video IoT micro-services 30 may be deployed, monitored,and/or maintained. The micro-services 30 may be managed by anapplication programming interface gateway (e.g., an API gateway), and anapplication level layer 7 load balancer. The micro-services 30 may bestateless and may hold the implementation of various video applicationcapabilities ranging from analysis to discovery of metadata 18, andother similar video related mathematical and/or other operations. Insome cases, these video micro-services 30 may have their own localstorage or cache which allow for rapid computation and processing. Eachmicro-service of the plurality of micro-services 30 may include adefined interface as well as an articulated atomic purpose to solve aclient applications need. The connected video micro-services 30 may beable to scale horizontally and vertically based upon the evolution ofthe cloud tenant 20 as well as the number of sites 12 the cloud tenant20 supports.

In some cases, the video query engine 25 may produce context objects,which may include one or more objects within the metadata 18 that maynot be able to be identified. The video query engine 25 may be incommunication with a video context analyzer 33, and the video contextanalyzer 33 may receive the unidentified context objects and performfurther analysis on the metadata 18. The video context analyzer 33 maybe in communication with the message bus 24 and thus may have access tothe metadata 18 received from the IoT hub 21. In some cases, the videocontext analyzer 33 may include a module that may be used to identifythe previously unidentified context objects, and map the context objectsreceived from the video query engine 25 to the original metadata 18video data schema, such as, for example, video clips, clip segments,and/or storage units.

In some cases, the cloud tenant 20 may include a service discovery agent26, a recommender engine 27, and an annotation stream handler 57. Theservice discovery agent 26, the recommender engine 27, and theannotation stream handler 57 may all be in communication with themessage bus 24. The service discovery agent 26 may be configured todiscover and bind one or more services in the runtime based upon thedynamic need. For example, when the security analyst 16 submits a queryfrom one of the sites 12, the particular application the securityanalyst 16 and/or the site 12 utilizes may determine which type of codevideo micro services may be needed to satisfy the query. In such cases,the service discovery agent 26 may act as a broker between the connectedvideo micro-services 30 and the application used by the security analyst16 and/or the sites 12 to discover, define, negotiate, and bind theconnected video micro-services 30 to the application dynamically. Theservice discovery agent 26 may include a common language that allows theapplications used by the security analyst 16 and/or the sites 12, andthe components within the cloud tenant 20 to communicate with oneanother without the need for user intervention or explicitconfiguration. The service discovery agent 26 may maintain its ownmetadata which may be used for rapid identification and mapping.

The recommender engine 27 may be a subclass of information filteringsystem that seeks to predict the rating or preference a user (e.g., thesecurity analyst 16) of the applications may assign to an individualitem. The recommender engine 27 may be used to analyze and recommend theappropriate analytical or machine learning model that may be requiredfor the various applications based upon the search query submitted.

The annotation stream handler 57 may extract various types ofinformation about the video streams received from the message bus 24,and add the information to the metadata of the video stream. In someexamples, the annotation stream handler 57 may extract metadata, and inother cases, the annotation stream handler 57 may extract informationfrom the metadata 18 itself. The annotations provided by the annotationstream handler 57 may enable the applications used by the securityanalyst 16 and/or the sites 12 to browse, search, analyze, compare,retrieve, and categorize the video streams and/or the metadata 18 moreeasily. The applications may be able to utilize the annotations providedby the annotation stream handler 57 without disrupting the real timeeven processing data traffic.

As discussed, the service discovery agent 26 and the video query engine25 may be in communication with the connected video micro-services 30.The connected video micro-services 30 and the video query engine 25 mayfurther be in communication with a video data lake 31. The video datalake 31 may be a centralized repository that allows for the storage ofstructured and unstructured metadata 18 at any scale for later postprocessing. Thus, the metadata 18 may be able to be stored prior tohaving to structure, process, and/or run various analytics over themetadata 18. The video data lake 31 may be able to scale horizontallyand vertically based upon the dynamic demand as well as the number ofsites 12 connected to the cloud tenant 20.

The cloud tenant 20 may include an analytics model store 32, which maybe in communication with the connected video micro-services 30 and thevideo query engine 25. The analytics model store 32 may be a centralizedstorage repository for storing a plurality of analytical and/or machinelearning models related to the video cognitive services 29, which enablethe video query engine 25 to understand natural language. The modelspresent in the analytics model store 32 may be continuously trained,tuned, and evolved based upon the availability of new datasets from eachof the plurality of sites 12. The video applications used by the sites12 may link to the analytical and/or the machine learning models presentin the analytics model store 32 to maintain the consistency of theinference and accuracy levels. Models present in the analytics modelstore 32 may be shared across multiple applications at the same time.

FIG. 3 is a schematic view of the illustrative video query engine 25 ofthe illustrative cloud tenant 20 in communication with the securityanalyst 16. As discussed with reference to FIG. 2 , the video queryengine 25 may receive a search query 34 from a user (e.g., the securityanalyst 16). For example, a user may input a search query 34 into a userinterface (not shown) which may be provided by a number of portabledevices, such as for example, but not limited to, remote internetdevices, including a smart phone, a tablet computer, a laptop computer,a desktop computer, or a workstation. The search query 34 may be enteredusing natural human language (e.g., find a man wearing a red shirt,carrying a briefcase). The video query engine 25 may include one or morevideo cognitive interfaces 28. Within the video cognitive interfaces 28,the video query engine 25 may analyze the query, at block 35, andconvert the natural human language into a computer search language. Thecomputer search query 34 may be applied to the time-stamped metadata 18stored within the memory 60 of the cloud tenant 20, and the video queryengine 25 may mine the metadata 18 and return a result to the user(e.g., security analyst 16). In some cases, the search query 34 may beapplied over a Spatial Temporal Regional Graph (STRG), as discussedfurther with reference to FIG. 4 . The video query engine 25 may minethe metadata 18 using the video cognitive services 29. In some cases,the video cognitive services 29 may utilize the machine learningapplication 59 to be able to better understand the user's intent for thesearch based on the user's context. For example, analyzing the query, atblock 35, may include the video cognitive interfaces 28 defining aninference for the search query 34, such as for example, a user's intent36, one or more entities 37, a user's emotion 38, and/or a context 39 ofthe search query 34. The user's intent 36 may represent a task or actionthe security analyst 16 (e.g., the user) intents to perform. The one ormore entities 37 may represent a word or phrase within the search query34 to be extracted, such as a primary object (e.g., a water bottle, anairport, etc.) and/or event of interest to the user. The user's emotion38 may include an attempt to extract the tone of the search query tobetter understand the user's intent 36, and the context 39 may representthe context of the search query within a broader conversation (e.g.differences between prior search queries) that has been invoked,sometimes along with some level the situational awareness (e.g.,situational context).

The video cognitive services 29 may apply the search query 34 to themetadata 18, utilize the machine learning application 59 (as shown inFIG. 2 ) to better define the user's intent 36, etc., and may search forone or more objects and/or events in the plurality of video streamsand/or metadata 18 that match the search query 34. The video cognitiveservices 29 may subsequently use API-mashups to interact with theconnected video micro services 30 and the video data lake 31 to returnthe result to the search query 34. The video cognitive services 29 mayprovide feedback to the user (e.g., security analyst 16) to furtherrefine the search query 34. The user (e.g., security analyst 16) mayinteract with the video cognitive services 29 via the user interface toprovide user feedback, wherein the feedback may include, for example, asubsequent search query that is entered after the video query engine 25returns the initial search result to the user (e.g., security analyst16). The feedback provided by the user may be used by the videocognitive services 29 within the video query engine 25 to better refinethe user search query 34 and return a result that more accuratelymatches the user intent of the search query 34.

The result returned to the user may identify one or more matchingobjects and/or events within the plurality of video data streams and/orthe metadata 18 that match the search query 34. For each matching objectand/or event that matches the search query 34, the video query engine 25may provide a link, such as for example, a hyperlink or a reference,corresponding to the video stream that includes the matching objectand/or event. The link provided may correspond to one of the pluralityof remote sites 12 within the video management system 10. The user(e.g., security analyst 16) may use the link to download a video clip ofthe video data stream that includes the matching object and/or eventfrom the corresponding remote site 12. The video clip may then beoutputted to the user via the user interface for display. The user maythen view the matching clip and determine if the search result matchesthe search query 34. The user may provide feedback to the video queryengine 25 within the cloud tenant 20 indicating whether or not thesearch result matches the search query 34.

The video query engine 25 may utilize the video cognitive services 29 tolearn over time such that the video query engine 25 may recognize videoobjects such as human dimensions (e.g., height, gender, race, weight,etc.), human wearables (e.g., shoes, hat, dress, etc.), objects carriedby a human (e.g., bag and type of bag, an umbrella, a book, a weapon,etc.), a vehicle (e.g., a car, a van, a bus, a bicycle, the color of thevehicle, etc.), and real world dimensions (e.g., distance, height,length, etc.). By using the feedback provided by the user, the videocognitive services 29 along with the machine learning application 59,may continually update its stored database, stored within the memory 60of the cloud tenant 20, thereby providing more accurate search resultsover time to the received search queries 34.

FIG. 4 is a schematic block diagram showing an illustrative SpatialTemporal Regional Graph (STRG) method showing receiving and storingmetadata from one or more remotely located sites (e.g., sites 12). Asshown in FIG. 4 , a video surveillance camera 48, which may berepresentative of the surveillance cameras located at the plurality ofremote sites 12, may capture one or more video data streams at itsrespective site. The camera 48 may be coupled to a memory which mayinclude for example, a security events storage 40, a video clips storage41 and a metadata storage 42. The camera 48 may further be coupled toone or more processors which may be operatively coupled to the memory.The camera 48 may capture one or more video data streams, and may sendthe one or more video data streams to the one or more processors. Insome cases, the one or more processors may be a part of the camera 48.In some cases, the one or more processors may be remote from the camera48 and the site 12. In some cases, the one or more processors maycommunicate with or control one or more components (e.g., the camera 48)of the video management system 10 via a wired or wireless link (notshown). The one or more processors may include one or more applicationswhich may be configured to process the one or more video data streams soas to extract information, and group the one or more video data streams.For example, the one or more processors may receive the one or morevideo data streams and extract object information, at block 44. Theextracted information may include, for example, feature values fromunstructured data such as colors, shapes, human gender, objects carriedby humans, vehicles, human wearables, and the like. The extractedfeatures may then be correlated with other objects, at block 45, such aspersons connected with the respective object, time and place of theevent and/or video data stream, a person's behavior and/or a routineover time, any material exchanges between correlated persons or objects,etc. The extracted and correlated objects (e.g., people, objects,vehicles, etc.) may then be grouped and/or indexed accordingly, at block46. For example, the objects may be indexed by incorporating variouslevels of the video objects, such as for example, blocks, regions,moving objects, scene levels, etc., for faster retrieval of information.Once grouped, the video objects may be converted to video metadata, atblock 47. The metadata may then be stored in a metadata storage 42(e.g., a memory). The metadata storage 42 may include metadata stored asstructural RAG, STRG, OG, and/or BG representation for later searchingpurposes. The metadata may further be stored in a timeline (e.g.timestamped).

In some cases, as discussed, the camera 48 may be coupled to a memorywhich may include for example, the video clips storage 41. The videodata streams captured by the camera 48 may be stored in the video clipsstorage 41 at the site 12. In some cases, the stored video data streamsmay be subjected to video object extraction, at block 44. Upon the videoobject extraction, the video data streams may then be correlated withother objects, at block 45, grouped, at block 46, converted to metadata,at block 47, and ultimately stored in the metadata storage 42. In somecases, the video data streams received from the camera 48 may enter arules engine 43. The rules engine 43 may include an additional layer inwhich the video data streams may be indexed by type of event. Forexample, a suspicious person carrying a weapon, or person loitering, ortwo or more people in an altercation, etc. may be indexed and storedwithin the security events storage 40, which may allow a securitypersonally quicker access to the particular event.

FIG. 5 is a schematic block diagram showing an illustrative method forreturning a search result based on a video query of the stored metadata,received from the one or more sites. As shown in FIG. 5 , a securityanalyst 52 may enter a search query at a workstation. The securityanalyst 52 may have access to a criminal database 50 and additionalexternal video input 51, as well as the video clips storage 41. Thesecurity analyst 52 may enter the search query into the video managementsystem, and a video query processor 53 may apply the search query to thevideo data from the criminal database 50, the external video input 51and the video clips storage 41. The video query processor 53 may extractthe relevant video data streams, at block 54, and may send the relevantvideo data streams to a video object search engine 55. The video objectsearch engine 55 may be an example of the video query engine 25. Thevideo object search engine 55 may have access to the video clips storage41, the metadata storage 42 and the security events storage 40. In somecases, the video object search engine 55 may be within the cloud tenant20. In some cases, the video object search engine 55 may be locatedwithin the one or more processors at each respective site 12. The videoobject search engine 55 may apply the search query to the video datastreams and the metadata received from the video clips storage 41, themetadata storage 42, the security events storage 40, and relevant datastreams received from block 54, and may output a video query resultlist, at block 56.

In some cases, the video object search engine 55 may utilizeunsupervised learning algorithms, such as clustering and conceptualclustering to find the complex relationships and meaningful informationin video objects for efficient access to the relationships of videoobjects across the multiple video data sources and/or streams (e.g., themetadata received from the video clips storage 41, the metadata storage42, the security events storage 40, and relevant data streams receivedfrom block 54). The video object search engine 55 may generatetime-stamped metadata which may be presented in the video query resultlist, at block 56. In some cases, the video query result list mayinclude one or more links (e.g., a reference or a hyperlink) tocorresponding video streams that match the search query.

FIG. 6 is a flow diagram showing an illustrative method 600 for sendingtime-stamped metadata corresponding to a video stream across acommunication path having a limited bandwidth. The illustrative method600 includes the video management system generating time-stampedmetadata for a first reference video frame of the plurality ofsequential video frames of the video stream, the time-stamped metadatafor the first reference video frame identifying objects detected in thefirst reference video frame, as referenced at block 610. The videomanagement system may send the time-stamped metadata for the firstreference video frame across the communication path, as referenced atblock 620. The video management system may subsequently generatetime-stamped metadata for each of a plurality of first delta videoframes following the first reference video frame, wherein thetime-stamped metadata for each of the plurality of first delta videoframes identifies changes in detected objects relative to the objectsidentified in the time-stamped metadata for the first reference videoframe, as referenced at block 630. The time-stamped metadata for each ofthe first reference video frame and each of the plurality of first deltavideo frames may identify objects and associations between objectsidentified in the corresponding video frame. The associations betweenobjects may include, for example, a distance between objects. In somecases, for each object detected, the time-stamped metadata may identifya unique object identifier along with one or more of an objectdescription, an object position, an object size, and the associationwith one or more other detected objects. The time-stamped metadata foreach of the plurality of first delta frames may identify changes indetected objects relative to the object identified in the time-stampedmetadata for the first reference video frame, by identifying a change inthe object's position, size, association with one or more other detectedobjects, and/or by identifying a new object that is not present in thefirst reference video frame. In some cases, the associations betweenobjects may be represented using a Spatial Temporal Regional Graph(STRG) in the time-stamped metadata.

The time-stamped metadata for each of the plurality of first delta videoframes may be sent across the communication path, as referenced at block640. In some cases, the video management system may further generatetime-stamped metadata for a second reference video frame of theplurality of sequential video frames of the video stream, wherein thesecond reference video frame follows the plurality of first delta videoframes, and the time-stamped metadata for the second reference videoframe identifies objects detected in the second reference video frame,as referenced at block 650. In some cases, the number of the pluralityof first delta video frames following the first reference video frameand the number of the plurality of second delta video frames followingthe second reference video frame may be the same. In some cases, thenumber of the plurality of first delta video frames following the firstreference video frame and the number of the plurality of second deltavideo frames following the second reference video frame may bedifferent. In some cases, the number of the plurality of first deltavideo frames following the first reference video frame is dependent onan amount of time-stamped metadata generated for each of the pluralityof first delta video frames relative to an expected size of thetime-stamped metadata if a new reference video frame were taken. Thevideo management system may then send the time-stamped metadata for thesecond reference video frame across the communication path, asreferenced at block 660. In some cases, when the video management systemgenerates the time-stamped metadata for the second reference frame, thevideo management may generate time-stamped metadata for each of aplurality of second delta video frames following the second referencevideo frame, wherein the time-stamped metadata for each of the pluralityof second delta video frames identifying changes in detected objectsrelative to the objects identified in the time-stamped metadata for thesecond reference video frame, as referenced at block 670, and then thevideo management system may send the time-stamped metadata for each ofthe plurality of second delta video frames across the communicationpath, as referenced at block 680.

FIGS. 7A-7C illustrate sequential scenes including one or more objects.The scenes in FIGS. 7A-7C may be illustrative of the method 600. Forexample, the scenes shown in FIGS. 7A-7C may illustrate how the videomanagement system 10 may send time-stamped metadata corresponding to avideo stream across a communication path having a limited bandwidth. Insome cases, the video management system 10 may generate time-stampedmetadata for a first reference frame 70. The time-stamped metadata forthe first reference frame 70 may identify objects detected in the firstreference frame 70. The video management system 10 may store thetime-stamped metadata for the first reference frame 70 in a memory. Inone example, the scene shown in FIG. 7A may be considered to be thereference frame 70. The reference frame 70 may be a first referencevideo frame of a plurality of sequential video frames (e.g., sequentialscenes) of the video stream, wherein the first reference frame 70includes the time-stamped metadata.

In some cases, the video management system may further generatetime-stamped metadata for each of a plurality of first delta frames 79 aand 79 b following the first reference frame 70. The time-stampedmetadata for each of the delta frames 79 a, 79 b may identify changes inthe detected objects relative to the objects identified in thetime-stamped metadata for the first reference frame 70. FIGS. 7B and 7Cdepict each of one of the plurality of delta video frames 79 a and 79 b,following the first reference video frame 70. The time-stamped metadatafor each of the delta frames 79 a, 79 b may be stored in a memory. Thetime-stamped metadata for the reference frame 70 and each of the deltaframes 79 a, 79 b may be sent to the cloud tenant 20 via a communicationpath. In some cases, sending the reference frame 70 and each of thedelta frames 79 a, 79 b may reduce the amount of bandwidth required tosend the frames, particularly when there are not significant changesfrom frame to frame. When no changes are detected, no metadata may besent for the delta frame.

In some cases, the time-stamped metadata for the reference frame 70and/or the delta frames 79 a, 79 b may identify a unique objectidentifier along with one or more of an object description, an objectposition, and/or an object size. In some cases, the metadata for each ofthe reference frame 70 and the delta frames 79 a, 79 b may identifyassociations between the objects identified in the corresponding videoframe, and may identify changed in the detected objects by identifying achange in an object's position and/or size. The associations mayinclude, for example, a distance between the objects. In some cases, theassociations may be represented using a Spatial Temporal Regional Graph(STRG).

In some cases, reference frames (e.g., reference frame 70) may containall the objects in the frame/scene, and the delta frames (e.g., deltaframes 79 a, 79 b) may include only the object information that differsfrom the reference frame (e.g., reference frame 70). For example, thefirst reference frame 70 may identify objects detected in the firstreference frame 70, such as, for example, a first car 71, a second car72, a third car 73, a first person 74, a second person 75, and a thirdperson 76. FIG. 7B may identify objects detected in the delta frame 79 ain which the objects detected differ from the reference frame 70, suchas, for example, the third car 73 has changed position in the frame, thefirst person 74 has changed position in the frame, the third person 76has changed position, and a plurality of new people 77, and 78 haveentered the frame. FIG. 7C may identify objects detected in the deltaframe 79 b in which the objects detected differ from the delta frame 79a, such as, for example, the second car 72 has returned and the firstperson 74 has changed position.

In some cases, the time-stamped metadata for each frame in the pluralityof sequential video frames (e.g., reference frame 70, delta frames 79 a,and 79 b) may include a frame reference that monotonically increase innumber. For example, the reference frame 70 may be labeled as RFn, andthe subsequent delta frames 79 a, 79 b may be labeled as DFn (wherein nrepresents a number). Further, a refresh period within the plurality ofsequential frames may be labeled as N. For example, with reference toFIGS. 7A-7C, the sequence of frames would be labeled {RF1, DF1, DF2}, asthe delta frames 79 a, 79 b differ from the reference frame 70. In somecases, where there is no differences between the first reference frame(e.g., reference frame 70) and the subsequent ten frames, the sequenceof frames may be labeled {RF1, DF11, DF12, DF13, DF14, DF15, N, RF2,DF21, DF22 . . . }. The refresh period N may be selected based on thenumber of delta frames following the previous reference frame when thevideo stream is sent across a communication path having a limitedbandwidth.

In some cases, the objects detected within each of the video frames,such as the reference frame 70 and the delta frames 79 a, 79 b, may bedecomposed into object attributes. Object attributes may include, forexample, a person, a gender, a race, a color of clothing, wearable items(e.g., a hand bag, shoes, a hat, etc.) a gun, a vehicle, a backgroundscene description, and the like. The object attributes may furtherinclude the following elements that may further describe the objectattributes within the video frames. For example, the elements of theobject attributes may include, a unique object identifier (ON), anobject description (OD), an object position (OP), an object associationwith other detected objects in the video frame (OE), and an object size(OS). The object identifier may describe a recognized objectidentification for reference (e.g., a person, a vehicle, a wearableitem, etc.). The object description may further describe the recognizedobject (e.g., a gender, a type of vehicle, a color and/or type of thewearable item, etc.). The object position may define a positioninformation of the recognized object within the scene, wherein theposition information is the relative coordinate information of the quadsystem of coordinates (e.g., −1, −1 to 1, 1). The object association maydefine the relationship between the recognized object with one or moreother detected objects within the scene, wherein the relationship isderived using the distance/time between the recognized object and theone or more additional detected objects within the scene, and the scenedepth. The object size may define the size of the recognized object byderiving the height of the object and/or a number of pixels of theboundary.

A graph model (e.g., a STRG) may be formulated for each video frameand/or scene using both the object identifier and the objectrelationships. For example, FIG. 7A may include six object identifiers(e.g., recognized objects), such as, the first car 71, the second car72, the third car 73, the first person 74, the second person 75, and thethird person 76. Each object may include an object description, whichwill be represented as STRG graph edges. The description may be, forexample, the third car 73 is a white minivan. Further, the objectrelationships will be derived between each object within the scene. Forexample, the first car 71 may be at a position of −0.24, 0.27 and thesecond car may be at a position of 0.24, 0.32. The delta frames 79 a and79 b in FIGS. 7B and 7C may include any changes in the objectassociations, and any new objects that have entered the scene (e.g., theplurality of new people new people 77, and 78). The associations may berepresented as STRG graph nodes and edges, and weights may be capturedbased upon concrete associations between the edges of the graphs. Thegraph elements may be continuously built based upon this information,and may be transmitted as metadata to the fast time series store 23.Video data may decouple from the underlying metadata, and multiplegraphs may be created for each delta frame (e.g., 79 a, 79 b) based uponthe reference frame (e.g., 70). In some cases, when a video query hasbeen performed, the graphs may be utilized within the video cognitiveservices 29 to refine the resultant video clip packet reference, whichmay reduce the time-stamped metadata corresponding to the video streamsent across the communication path having the limited bandwidth.

In some cases, the video management system 10 may generate time-stampedmetadata for a second reference video frame (not shown). The secondreference video frame may follow the plurality of first delta frames 79a, 79 b. The second reference video frame may identify objects detectedin the second reference video frame. The video management system 10 maystore the time-stamped metadata in the memory. The video managementsystem may further generate time-stamped metadata for each of aplurality of second delta video frames (not shown) following the secondreference frame. The time-stamped metadata for each of the plurality ofsecond delta frames may identify changed in the detected objectsrelative to the objects identified in the time-stamped metadata for thesecond reference video frame. The video management system 10 may thenstore the time-stamped metadata in the memory. The time-stamped metadatafor the second reference frame and the second delta video frames may besent across a communication path to the cloud tenant 20 using limitedbandwidth.

FIG. 8 is a flow diagram showing an illustrative method 800 forsearching for one or more objects and/or events in one or more videostreams. The method 800 may include receiving time-stamped metadata fora video stream, wherein the time-stamped metadata identifies one or moreobjects and/or events occurring in the video stream, as referenced atblock 810. A user may then enter a query into a video query engine,wherein the video query engine includes one or more cognitive models, asreferenced at block 820. The video query engine may process the userquery using the one or more cognitive models to build a search query, asreferenced at block 830, and the video query engine may apply the searchquery to the time-stamped metadata via the video query engine to searchfor one or more objects and/or events in the video stream that matchesthe search query, as referenced at block 840. The video query engine maythen return a search result to the user, wherein the search result mayidentify one or more matching objects and/or events in the video streamthat match the search query, as referenced at block 850, and may displaya video clip that includes at least one of the one or more matchingobjects and/or events, as referenced at block 860. The video queryengine may further process the time-stamped metadata to identifycontextual relationships between objects and/or events occurring in thevideo stream before entering the user query, as referenced at block 870.

FIGS. 9 and 10 are schematic block diagrams showing an illustrativemethods 80 and 90, respectively, for intelligent machine learning. Asdiscussed with reference to FIGS. 2 and 3 , the video query engine 25may include the machine learning application 59 and the video cognitiveservices 29. The video cognitive services 29 may be configured withnatural language understanding capabilities, and may be continuallyupdated by the machine learning application 59 using reinforcementlearning mechanisms, such as methods 80 and 90, for example. The machinelearning application 59 may integrate Bayesian belief tracking andreward-based reinforcement learning methods to derive a user's intentwhen entering a search query. The intent of the user is considered to bea hidden variable and may be inferred from knowledge of the transitionand the observation probabilities of the observed search queries. Toderive the hidden variable (e.g., the user's intent), the followingequations may be applied:

Let the distribution of the hidden state st−1 at time t−1 be denoted bybt−1(st−1), then the inference problem is to find bt(st) given bt−1 at−1and ot. This is easily solved using Bayes' rule,

$\begin{matrix}{{{{bt}({st})} = {{P\left( {{{st}❘{ot}},{{at} - 1},{{bt} - 1}} \right)} = {{{p\left( {{ot}❘{st}} \right)}{{P\left( {{{st}❘{{at} - 1}},{{bt} - 1}} \right)}/{p\left( {{{ot}❘{{at} - 1}},{{bt} - 1}} \right)}}} = {{{{p\left( {{ot}❘{st}} \right)}{\sum{st}}} - {1{{P\left( {{st},{{{st} - 1}❘{{at} - 1}},{{bt} - 1}} \right)}/{p\left( {{{ot}❘{{at} - 1}},{{bt} - 1}} \right)}}}} = {{{k.{p\left( {{ot}❘{st}} \right)}}{\sum{st}}} - {1{P\left( {{{st}❘{{st} - 1}},{{at} - 1}} \right)}{bt}} - {1\left( {{st} - 1} \right)}}}}}},} & (1)\end{matrix}$

where k=1/p(ot|at−1,bt−1) is a normalization constant. The distributionof states is often denoted by an N−dimensional vector b=[b(s1), . . . ,b(sN)]′ called the belief state. The belief update can then be writtenin matrix form as:

=k.O(ot)T(at−1)bt−1

where T(a) is the N×N transition matrix for action a, andO(o)=diag([p(o|s1), . . . , p(o|sN)]) is a diagonal matrix ofobservation probabilities. Thus, the computational complexity of asingle inference operation is O(N2+3N) including the normalization.

The choice of specific rewards is a design decision and differentrewards will result in different policies and differing userexperiences. The choice of reward function may also affect the learningrate during policy optimization. However, once the rewards have beenfixed, the quality of a policy is measured by the expected total rewardover the course of the user interaction:

R=E{Σt=1Σsbt(s)r(s,at)}=E{Σt=1Tr(bt,at)}

if the process is Markovian, the total reward Vπ(b) expected intraversing from any belief state b to the end of the interactionfollowing policy π it is independent of all preceding states. UsingBellman's optimality principle; it is possible to compute the optimalvalue of this value function iteratively:

V*(b)=maxa{r(b,a)+Σop(o|b,a)V*(τ(b,a,o))}

Where τ(b,a,o) represents the state update function. This iterativeoptimization is an example of reinforcement learning. This optimal valuefunction for finite interaction sequences is piecewise-linear andconvex. It can be represented as a finite set of N−dimensionalhyperplanes spanning belief space where each hyperplane in the set hasan associated action. This set of hyperplanes also defines the optimalpolicy since at any belief point b all that is required is to find thehyperplane with the largest expected value V*(b) and select theassociated action.

In use, the method 80 shown in FIG. 9 illustrates an agent 81 (e.g., thevideo query engine 25) that may provide an action 82 (e.g. a searchresult) to a user 83. In some cases, when the action 82 does not matchthe search query, the user 83 may provide a reinforcement 84, which maybe in the form of feedback, which may include a subsequent user query,to the agent 81. The agent 81 may provide a subsequent action 82 to theuser 83. In some cases, when the action 82 (e.g., the search result)matches the search query entered by the user 83, the user 83 may notprovide any feedback. Thus, the agent 81 receives a state 85notification and the agent 81 may store the search query and the action82 presented for future reference.

The method 90, as shown in FIG. 10 , may be similar to the method 80 ofFIG. 9 . However, the method 90 may differ from the method 80 in that anagent 91 (e.g., the video query engine 25) may apply an action 92 to anoriginal image 93, to produce a segmented image 94. The segmented image94 may be presented to a user (not shown). In some cases, when thesegmented image 94 does not match the search query, the user may providea reward and/or a punishment 96, which may be in the form of feedback,which may include a subsequent user query, to the agent 91. The agent 91may provide a subsequent action 92 to the original image 93, therebyproducing a second segmented image 94. In some cases, when the segmentedimage 94 matches the search query entered by the user, the user may notprovide any reward and/or a punishment 96. Thus, the agent 91 receives astate 95 notification and the agent 91 may store the search query andthe segmented image 94 presented for future reference.

FIGS. 11A and 11B show illustrative screens 100 and 110 respectively, inwhich a user may enter a query to search for one or more objects and/orevents in one or more video streams (e.g., FIG. 11A), and the output ofthe query entered (e.g., FIG. 11B). As discussed, a user may enter asearch query in natural language, as shown in FIG. 11A. For example, theuser may enter a search text 101 stating for example, “wearing darksuit, dark tie”. The search may enter the video query engine 25 withinthe cloud tenant 20, and ultimately may provide a search result list 102to the user. The search result list 102 may include a time-stamp 103, acamera number 104, and a site Id 105. The user may select one of thesearch results within the search result list 102. In one example, theuser may select the first search result within the search result list102. The first search result may include a time-stamp 103 a indicatingthe video clip is from 08/17/2016 at 11:06:04. The video clip may bereceived from camera number 11, as indicated by 104 a, and site 3, asindicated by 105 a. The resultant video clip, as shown in FIG. 11B mayinclude a screen 110 showing a man 111 wearing a dark suit and a darktie. The user may indicate whether or not the search result matches thesearch query entered, as discussed with reference to FIGS. 9 and 10 .The user may select each of the search results within the search resultlist 102 to determine which clips are relevant to the entered searchquery.

In some cases, a link may be provided in the search results. In theexample shown, the timestamp 103 a may encode a hyperlink that includesan address to the corresponding video stream (Camera Number 11) at aremote site (Remote Site 3) with a reference time that includes thematching object and/or event. The link, when selected by the user, mayautomatically download the video clip of the video stream that includesthe matching object and/or event from the corresponding remote site, andthe video clip of the video stream may be displayed on a display foreasy using by the user.

FIG. 12 is a flow diagram showing an illustrative method 200 forsearching for one or more events in a plurality of video streamscaptured and stored at a plurality of remote sites. In some cases, theplurality of remote sites may be geographically dispersed. In othercases the plurality of remote sites may be in the same geographicregion. The method 200 may include a video management system generating,at each of the plurality of remote sites, time-stamped metadata for eachvideo stream captured at the remote site, wherein the time-stampedmetadata for each video stream identifies one or more objects and/orevents occurring in the corresponding video stream as well as anidentifier that uniquely identifies the corresponding video stream, asreferenced at block 205. The identifier that uniquely identifies thecorresponding video stream in the time-stamped metadata may include anaddress, and/or the remote site that stores the corresponding videostream as well as a source of the corresponding video stream.

Each of the plurality of remote sites may send the time-stamped metadatato a central hub, wherein the time-stamped metadata is stored in a datalake, as referenced at block 210. The central hub may be located in thecloud, and may be in communication with the plurality of remote sitesvia the Internet. A user may enter a query into a video query engine,wherein the video query engine is operatively coupled to the centralhub, as referenced at block 215, and the central hub may apply the queryto the time-stamped metadata stored in the data lake to search for oneor more objects and/or events in the plurality of video streams thatmatch the query, as referenced at block 220. In some cases, the centralhub may execute the video query engine. The video query engine mayinclude one or more cognitive models to help derive an inference for thequery to aid in identifying relevant search results. In some cases, theinference may be one or more of a user's intent of the query, a user'semotion, a type of situation, a resolution, and a context of the query.The one or more cognitive models may be refined using machine learningover time.

The video management system may return a search result to the user,wherein the search result identifies one or more matching objects and/orevents in the plurality of video streams that match the query, and foreach matching object and/or event that matches the query, providing alink to the corresponding video stream with a reference time thatincludes the matching object and/or event, as referenced at block 225.The link may be used to download a video clip of the video stream thatincludes the matching object and/or event from the corresponding remotesite, as referenced at block 230, and the video clip of the video streammay be displayed on a display, as referenced at block 235. In somecases, the link may include a hyperlink or other reference. In somecases, the link may be automatically initiated when the search result isreturned to the user, such that the corresponding video clip thatincludes the matching object and/or event is automatically downloadedfrom the corresponding remote site that stores the corresponding videostream and displayed on the display. In some cases, the central hub mayfurther process the time-stamped metadata stored in the data lake toidentify additional objects and/or events occurring in the plurality ofvideo streams captured at the plurality of remote sites, as referencedat block 240, and the central hub may process the time-stamped metadatastored in the data lake to identify contextual relationships betweenobjects and/or events occurring in the plurality of video streamscaptured at the plurality of remote sites, as referenced at block 245.

FIG. 13 is a flow diagram showing an illustrative method 300 forreceiving time-stamped metadata corresponding to a video stream across acommunication path having a limited bandwidth. The method 300 mayinclude receiving time-stamped metadata for a first reference videoframe of the plurality of sequential video frames of the video stream,wherein the time-stamped metadata for the first reference video frameidentifies objects detected in the first reference video frame, asreferenced at block 310. The time-stamped metadata for each of aplurality of first delta video frames following the first referencevideo frame may be received, and the time-stamped metadata for each ofthe plurality of first delta video frames may identify changes indetected objects relative to the objects identified in the time-stampedmetadata for the first reference video frame, as referenced at block320. The time-stamped metadata for each of the first reference videoframe and the plurality of first delta video frames may identify objectsand associations between objects identified in the corresponding videoframe.

The method 300 may further include receiving time-stamped metadata for asecond reference video frame of the plurality of sequential video framesof the video stream, the second reference video frame following theplurality of first delta video frames, the time-stamped metadata for thesecond reference video frame identifying objects detected in the secondreference video frame, as referenced at block 330. The time-stampedmetadata for each of a plurality of second delta video frames followingthe second reference video frame may be received, and the time-stampedmetadata for each of the plurality of second delta video frames mayidentify changes in detected objects relative to the objects identifiedin the time-stamped metadata for the second reference video frame, asreferenced at block 340, and the received time-stamped metadata may beprocessed to identify one or more events in the video stream, asreferenced at block 350.

FIG. 14 is a flow diagram showing an illustrative method 400 forsearching for one or more objects and/or events in one or more videostreams. The method 400 may include receiving time-stamped metadata foreach of the one or more video streams, the time-stamped metadata foreach video stream identifying one or more objects and/or eventsoccurring in the corresponding video stream as well as an identifierthat uniquely identifies the corresponding video stream, as referencedat block 410. A user may enter a query into a video query engine,wherein the video query engine includes one or more cognitive models, asreferenced at block 420. The one or more cognitive models of the videoquery engine may be refined using machine learning over time, and insome cases, machine learning over time based on user feedback. The userfeedback may include a subsequent user query that is entered after thevideo query engine returns the search result to the user in order torefine the user query. The video query engine may process the user queryusing the one or more cognitive models to identify an inference for theuser query, as referenced at block 430. The inference may include the auser's intent of the user query, an emotional state of the user thatentered the query, a situational context in which the user query wasentered, and which entities are the primary objects and/or events ofinterest to the user that entered the user query.

The video query engine may build a search query based at least in parton the user query and the identified inference, as referenced at block440, and the video query engine may apply the search query to thetime-stamped metadata via the video query engine to search for one ormore objects and/or events in the one or more video streams that matchthe search query, as referenced at block 450. The video query engine maythen return a search result to the user, wherein the search resultidentifies one or more matching objects and/or events in the one or morevideo streams that match the search query, and for each matching objectand/or event that matches the search query, providing a reference to thecorresponding video stream and a reference time in the correspondingvideo stream that includes the matching object and/or event, asreferenced at block 460, and for at least one of the matching objectand/or event that matches the search query, using the reference to thecorresponding video stream and the reference time to identify anddisplay a video clip that includes the matching object and/or event, asreferenced at block 470. In some cases, the reference to thecorresponding video stream and the reference time may be used toidentify and display the video clip that includes the matching objectand/or event may be initiated automatically upon the video query enginereturning the search result. In some cases, the reference to thecorresponding video stream and the reference time may be used toidentify and display the video clip that includes the matching objectand/or event may be initiated manually by a user after the video queryengine returns the search result.

The method 400 may further include receiving time-stamped data generatedby one or more non-video based devices, such as, for example, one ormore security sensors, and the one or more cognitive models using thetime-stamped data generated by one or more non-video based devices toidentify an inference for the user query, as referenced at block 480.The one or more cognitive models may use the time-stamped data generatedby the one or more non-video based devices and time-stamped metadata forone or more video streams to identify the inference for the user query.The method 400 may include processing the time-stamped metadata toidentify contextual relationships between objects and/or eventsoccurring in the one or more video streams before entering the userquery, as referenced at block 490.

ADDITIONAL EMBODIMENTS

In one example, the plurality of remote sites may be geographicallydispersed sites.

Alternatively, or in addition, the central hub may be in the cloud andis in communication with the plurality of remote sites via the Internet.

Alternatively, or in addition, the central hub may execute the videoquery engine.

Alternatively, or in addition, by the central hub may process thetime-stamped metadata stored in the data lake to identify additionalobjects and/or events occurring in the plurality of video streamscaptured at the plurality of remote sites.

Alternatively, or in addition, the central hub may process thetime-stamped metadata stored in the data lake to identify contextualrelationships between objects and/or events occurring in the pluralityof video streams captured at the plurality of remote sites.

Alternatively, or in addition, the video query engine may include one ormore cognitive models to derive an inference for the query to aid inidentifying relevant search results.

Alternatively, or in addition, the inference may be one of a user'sintent of the query, a user's emotion, a type of user, a type ofsituation, a resolution, and a context of the query.

Alternatively, or in addition, the one or more cognitive models may berefined using machine learning over time.

Alternatively, or in addition, the identifier that uniquely identifiesthe corresponding video stream in the time-stamped metadata may includean address.

Alternatively, or in addition, the identifier that uniquely identifiesthe corresponding video stream in the time-stamped metadata may identifythe remote site that stores the corresponding video stream as well as asource of the corresponding video stream.

Alternatively, or in addition, the link may include a hyperlink or areference.

Alternatively, or in addition, the link may be automatically initiatedwhen the search result is returned to the user, such that thecorresponding video clip that includes the matching object and/or eventmay be automatically downloaded from the corresponding remote site thatstores the corresponding video stream and displayed on the display.

Alternatively, or in addition, the central hub may be in the cloud andmay be in communication with the plurality of remote sites via theInternet.

Alternatively, or in addition, the one or more processors may be furtherconfigured to process the time-stamped metadata stored in the memory toidentify additional objects and/or events occurring in the plurality ofvideo streams captured at the plurality of remote sites.

Alternatively, or in addition, the central hub may execute a video queryengine that may include one or more cognitive models to derive aninference for the query to aid in identifying relevant search results.

Alternatively, or in addition, the request may include the identifierthat uniquely identifies the corresponding video stream.

Alternatively, or in addition, the time-stamped metadata for each of thefirst reference video frame and each of the plurality of first deltavideo frames may identify objects and associations between objectsidentified in the corresponding video frame.

Alternatively, or in addition, the associations between objects mayinclude a distance between objects.

Alternatively, or in addition, the associations between objects may berepresented using a Spatial Temporal Regional Graph (STRG) in thetime-stamped metadata.

Alternatively, or in addition, for each detected object, thetime-stamped metadata may identify a unique object identifier along withone or more of an object description, an object position, and an objectsize.

Alternatively, or in addition, the time-stamped metadata for each of theplurality of first delta video frames may identify changes in detectedobjects relative to the objects identified in the time-stamped metadatafor the first reference video frame by identifying a change in anobjects position, size, and/or association with one or more otherdetected objects.

Alternatively, or in addition, for each detected object, thetime-stamped metadata may identify a unique object identifier along withone or more of an object description, an object position, an objectsize, and an association with one or more other detected objects.

Alternatively, or in addition, the time-stamped metadata for each of theplurality of first delta video frames may identify a change in detectedobjects relative to the objects identified in the time-stamped metadatafor the first reference video frame by identifying a new object that isnot present in the first reference video frame.

Alternatively, or in addition, the number of the plurality of firstdelta video frames following the first reference video frame and thenumber of the plurality of second delta video frames following thesecond reference video frame may be the same.

Alternatively, or in addition, the number of the plurality of firstdelta video frames following the first reference video frame and thenumber of the plurality of second delta video frames following thesecond reference video frame may be different.

Alternatively, or in addition, the number of the plurality of firstdelta video frames following the first reference video frame may bedependent on an amount of time-stamped metadata generated for each ofthe plurality of first delta video frames relative to an expected sizeof the time-stamped metadata if a new reference video frame were taken.

Alternatively, or in addition, the time-stamped metadata for each of thefirst reference video frame and each of the plurality of first deltavideo frames may identify objects and associations between objectsidentified in the corresponding video frame.

Alternatively, or in addition, the associations between objects mayinclude a distance between objects.

Alternatively, or in addition, the associations between objects may berepresented using a Spatial Temporal Regional Graph (STRG) in thetime-stamped metadata.

Alternatively, or in addition, for each detected object, thetime-stamped metadata may identify a unique object identifier along withone or more of an object description, an object position, and an objectsize.

Alternatively, or in addition, the time-stamped metadata for each of theplurality of first delta video frames may identify changes in detectedobjects relative to the objects identified in the time-stamped metadatafor the first reference video frame by identifying a change in anobjects position, size, and/or association with one or more otherdetected objects.

Alternatively, or in addition, for each detected object, thetime-stamped metadata may identify a unique object identifier along withone or more of an object description, an object position, an objectsize, and an association with one or more other detected objects.

Alternatively, or in addition, the time-stamped metadata for each of thefirst reference video frame and each of the plurality of first deltavideo frames may identify objects and associations between objectsidentified in the corresponding video frame.

Alternatively, or in addition, the inference may be to a user's intentof the user query.

Alternatively, or in addition, the inference may be to an emotionalstate of the user that entered the user query.

Alternatively, or in addition, the inference may be to a situationalcontext in which the user query was entered.

Alternatively, or in addition, the inference may be to which entitiesare the primary objects and/or events of interest to the user thatentered the user query.

Alternatively, or in addition, the one or more cognitive models of thevideo query engine may be refined using machine learning over time.

Alternatively, or in addition, the one or more cognitive models of thevideo query engine may be refined using machine learning over time basedon user feedback.

Alternatively, or in addition, the user feedback may include asubsequent user query that is entered after the video query enginereturns the search result to the use in order to refine the user query.

Alternatively, or in addition, time-stamped data generated by one ormore non-video based devices may be received, and the one or morecognitive models using the time-stamped data generated by one or morenon-video based devices may identify an inference for the user query.

Alternatively, or in addition, the one or more cognitive models may usethe time-stamped data generated by one or more non-video based devicesand time-stamped metadata for one or more of the one or more videostreams to identify an inference for the user query.

Alternatively, or in addition, one or more non-video based devices mayinclude one or more security sensors.

Alternatively, or in addition, processing the time-stamped metadata mayidentify contextual relationships between objects and/or eventsoccurring in the one or more video streams before entering the userquery.

Alternatively, or in addition, using the reference to the correspondingvideo stream and the reference time to identify and display the videoclip that includes the matching object and/or event may be initiatedautomatically upon the video query engine returning the search result.

Alternatively, or in addition, using the reference to the correspondingvideo stream and the reference time to identify and display the videoclip that includes the matching object and/or event may be initiatedmanually by a user after the video query engine returns the searchresult.

Alternatively, or in addition, the one or more cognitive models of thevideo query engine may be refined using machine learning over time.

Alternatively, or in addition, the one or more cognitive models of thevideo query engine may be refined using machine learning over time basedon user feedback, wherein the user feedback may include a subsequentuser query that is entered after the video query engine returns thesearch result to the use in order to refine the user query.

Alternatively, or in addition, the inference may be to one or more of: auser's intent of the user query, an emotional state of the user thatentered the user query, a situational context in which the user querywas entered, and which entities are the primary objects and/or events ofinterest to the user that entered the user query.

Alternatively, or in addition, processing the time-stamped metadata toidentify contextual relationships between objects and/or eventsoccurring in the video stream before entering the user query.

All numbers are herein assumed to be modified by the term “about”,unless the content clearly dictates otherwise. The recitation ofnumerical ranged by endpoints includes all numbers subsumed within thatrange (e.g., 1 to 5 includes, 1, 1.5, 2, 2.75, 3, 3.8, 4, and 5).

As used in this specification and the appended claims, the singularforms “a”, “an”, and “the” include the plural referents unless thecontent clearly dictates otherwise. As used in this specification andthe appended claims, the term “or” is generally employed in its senseincluding “and/or” unless the content clearly dictates otherwise.

It is noted that references in the specification to “an embodiment”,“some embodiments”, “other embodiments”, etc., indicate that theembodiment described may include a particular feature, structure, orcharacteristic, but every embodiment may not necessarily include theparticular feature, structure, or characteristic. Moreover, such phrasesare not necessarily referring to the same embodiment. Further, when aparticular feature, structure, or characteristic is described inconnection with an embodiment, it is contemplated that the feature,structure, or characteristic may be applied to other embodiments whetheror not explicitly described unless clearly stated to the contrary.

Having thus described several illustrative embodiments of the presentdisclosure, those of skill in the art will readily appreciate that yetother embodiments may be made and used within the scope of the claimshereto attached. It will be understood, however, that this disclosureis, in many respects, only illustrative. Changes may be made in details,particularly in matters of shape, size, arrangement of parts, andexclusion and order of steps, without exceeding the scope of thedisclosure. The disclosure's scope is, of course, defined in thelanguage in which the appended claims are expressed.

What is claimed is:
 1. A method for searching for one or more events ina plurality of video streams captured and stored at a plurality ofremote physical sites, the method comprising: capturing, from aplurality of cameras, a plurality of video streams, wherein each cameraof the plurality of cameras captures a respective video stream of theplurality of video streams, and wherein each camera of the plurality ofcameras is located at a respective remote physical site; for each videostream of the plurality of video streams, storing the video stream atthe remote physical site corresponding to the location of the camerathat captured the video stream; processing each of the plurality ofvideo streams at the remote physical site corresponding to the locationof the camera that captured the video stream, wherein processing each ofthe plurality of video streams includes identifying one or more objectsin the respective video stream and determining one or more objectattributes for each of the identified objects, wherein the one or moreobject attributes include one or more of: an object description of therespective object; a size of the respective object; a color of therespective object; a shape of the respective object; a position of therespective object within a respective video frame of the respectivevideo stream; one or more associations with one or more other objectsidentified in the respective video stream based at least in part on adistance between the identified objects in the respective video frameand/or time between the identified objects in the respective videostream; generating, at each remote physical site of the plurality ofremote physical sites, time-stamped metadata for each video streamcaptured at the remote physical site, the time-stamped metadata for eachvideo stream identifying one or more objects captured by the respectivevideo stream as well as an identifier that uniquely identifies therespective video stream; wherein, for one or more of the objectscaptured by the respective video stream, the time-stamped metadataincluding an object identifier for the respective object and one or moreobject attributes of the respective object including one or more of: theobject description of the respective object; the position of therespective object within the respective video frame of the respectivevideo stream; the size of the respective object; the color of therespective object; the shape of the respective object; one or moreassociations with one or more other objects identified in the respectivevideo stream based at least in part on a distance between the identifiedobjects in the respective video frame and/or time between the identifiedobjects in the respective video stream; sending, from each remotephysical site of the plurality of remote physical sites, thetime-stamped metadata over a network to a central hub located physicallyremote from the remote physical site; receiving, by the central hub, aquery entered by a user via a user device, and providing the query to avideo query engine; the video query engine applying the query to thetime-stamped metadata at the central hub; returning a search result tothe user device, wherein the search result identifies one or morematching objects identified by the time-stamped metadata in theplurality of video streams that match the query; identifying one or moreof the plurality of video streams that include one or more of thematching objects; downloading at least part of one of the plurality ofvideo streams that include one or more of the matching objects from therespective remote physical site; and displaying at least part of thedownloaded video stream on a display of the user device.
 2. The methodof claim 1, wherein the plurality of remote physical sites aregeographically dispersed sites.
 3. The method of claim 1, wherein thecentral hub is in the cloud and is in communication with the pluralityof remote physical sites via the Internet.
 4. The method of claim 1,wherein the central hub executes the video query engine.
 5. The methodof claim 1, further comprising processing, by the central hub, thetime-stamped metadata to identify additional objects in the plurality ofvideo streams captured at the plurality of remote physical sites beforethe video query engine applies the query to the time-stamped metadata.6. The method of claim 1, wherein the video query engine includes one ormore cognitive models to derive an inference for the query to aid inidentifying relevant search results.
 7. The method of claim 6, whereinthe inference is one of a user's current emotional state, a type ofsituation that is currently being addressed by the user, and a user'sdesired degree of resolution for the query.
 8. The method of claim 6,wherein the one or more cognitive models are refined using machinelearning over time.
 9. The method of claim 1, wherein the identifierthat uniquely identifies the respective video stream in the time-stampedmetadata includes a network address.
 10. The method of claim 1, whereinthe identifier that uniquely identifies the respective video stream inthe time-stamped metadata identifies the remote physical site thatstores the respective video stream as well as a source of the respectivevideo stream.
 11. The method of claim 1, wherein downloading the atleast part of one of the plurality of video streams is initiated by theuser via the user device.
 12. The method of claim 11, whereindownloading the at least part of one of the plurality of video streamsis initiated by the user clicking on a hyper link in the search result.13. The method of claim 1, wherein downloading the at least part of oneof the plurality of video streams is automatically performed whenreturning the search result to the user device.
 14. A central hub forsearching for one or more events in a plurality of video streams,wherein each of the plurality of video streams is captured by a cameralocated at a respective one of a plurality of remote physical sites, andwherein each of the plurality of video streams is stored at the remotephysical site that corresponds to the location of the camera thatcaptured the video stream, the central hub comprising: a memory; one ormore processors operatively coupled to the memory, the one or moreprocessors configured to: receive time-stamped metadata from each of theplurality of remote physical sites over a network, wherein thetime-stamped metadata is generated at each of the remote physical sitesby processing each of the of the respective video streams to identifyone or more objects in the respective video stream and generate thetime-stamped metadata for the respective video stream that identifiesthe one or more objects identified in the respective video stream aswell as an identifier that uniquely identifies the respective videostream; wherein, for at least one of the one or more objects identifiedin a respective video stream, the time-stamped metadata includes one ormore object attributes include one or more of: an object description ofthe respective object; a size of the respective object; a color of therespective object; a shape of the respective object; a position of therespective object within a respective video frame of the respectivevideo stream; one or more associations with one or more other objectsidentified in the respective video stream based at least in part on adistance between the identified objects in the respective video frameand/or time between the identified objects in the respective videostream store the received time-stamped metadata in the memory; receive aquery from a user via a user device; apply the query to the time-stampedmetadata; return a search result, wherein the search result identifiesone or more objects identified by the time-stamped metadata in theplurality of video streams that match the query; download at least partof one of the plurality of video streams that include one or more of thematching objects from the respective remote physical site; and displayat least part of the downloaded video stream on a display of the userdevice.
 15. The central hub of claim 14, wherein the one or moreprocessors are configured to provide a link in the returned searchresult, wherein the link, when initiated, initiates the download of avideo clip from the respective remote physical site that includes one ormore of the matching objects.
 16. The central hub of claim 14, whereinthe one or more processors are configured to: process the time-stampedmetadata from the plurality of remote physical sites to identify one ormore relationships between an object identified in the time-stampedmetadata of a first one of the plurality of remote physical sites and anobject identified in the time-stamped metadata of a second one of theplurality of remote physical sites, resulting in generation ofadditional time-stamped metadata; and apply the query to thetime-stamped metadata including the additional time-stamped metadata.17. The central hub of claim 14, wherein the plurality of remotephysical sites are geographically dispersed sites.
 18. The central hubof claim 14, wherein the central hub executes a video query engine thatincludes one or more cognitive models to derive an inference for thequery to aid in identifying relevant search results, wherein theinference is one of a user's current emotional state, a type ofsituation that is currently being addressed by the user, and a user'sdesired degree of resolution for the query.
 19. A remote physical siteincluding one or more cameras for capturing one or more video streams,the remote physical site comprising: a memory; one or more processorsoperatively coupled to the memory, the one or more processors configuredto: store the one or more video streams captured by the one or morecameras at the remote physical site in the memory; process each of theone or more video streams to identify one or more objects in therespective video stream and determine one or more object attributes foreach of the identified objects, wherein the one or more objectattributes include one or more of: an object description of therespective object; a size of the respective object; a color of therespective object; a shape of the respective object; a position of therespective object within a respective video frame of the respectivevideo stream; one or more associations with one or more other objectsidentified in the respective video stream based at least in part on adistance between the identified objects in the respective video frameand/or time between the identified objects in the respective videostream; generate time-stamped metadata for each of the one or more videostreams captured by the one or more cameras at the remote physical site,the time-stamped metadata for each video stream identifying one or moreobjects captured by the respective video stream as well as an identifierthat uniquely identifies the respective video stream; wherein, for oneor more of the one or more objects captured in the respective videostream, the time-stamped metadata including an object identifier for therespective object and one or more object attributes of the respectiveobject including one or more of: the object description of therespective object; the position of the respective object within therespective video frame of the respective video stream; the size of therespective object; the color of the respective object; the shape of therespective object; one or more associations with one or more otherobjects identified in the respective video stream based at least in parton a distance between the identified objects in the respective videoframe and/or time between the identified objects in the respective videostream; send the time-stamped metadata to a central hub that is locatedphysically remote from the remote physical site via a network withoutsending the one or more video streams stored at the remote physical siteto the central hub via the network; receive a request requesting atleast part of each of one or more of the one or more video streamsstored at the remote physical site; and in response to receiving therequest, send the requested part of each of one or more video streams.20. The remote physical site of claim 19, wherein the request isreceived from the central hub via the network, and the requested part ofeach of one or more video streams is sent to the central hub via thenetwork.